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Lean Six Sigm Program Nam

< Last Name, First Name > Wave No: Review Date:

DefineMeasure Analyze Improve Control

LEAN SIX SIGMA

DefineMeasure Analyze Improve Control

LEAN SIX SIGMA

Lean Six Sigm Program Nam

Analyze Phase Road MapDefine Measure Analyze Improve

Analyze

Control

Activities Identify Reduce Confirm

Tools Process

Potential Root Causes List of Potential Root Causes Root Cause to Output Relationship Impact of Root Causes on Key Root Causes Analyze Gate

Constraint ID and Takt Time

Analysis Cause FMEA Hypothesis Simple ANOVA Components Conquering

& Effect Analysis Tests/Conf. Intervals

Estimate

Outputs Prioritize Complete

& Multiple Regression of Variation

Product and Process

Complexity Queuing

Theory

Bonacorsi Consulting

3

Measure OverviewProcess Capability

Analyze

Graphical AnalysisI -MR Chart of Delivery Tim e40 UC L=37.70 In ivid a V lu d u l a e 35 30 25 20 1 28 55 82 109 LC L=20.56 136 Observation 163 190 217 244 _ X=29.13

CTQ: ? Unit (d) or Mean (c): ? Defect (d) or St. Dev. (c): ? DPMO (d): ? Sigma (Short Term): ?M ovin R n e g a g

Sigma (Long Term):? MSA Results: show the percentage result of the GR&R, AR&R or other measurement systems analysis carried out in the project

10.0 7.5 5.0 2.5 0.0 1 28 55 82 109 136 Observation 163 190 217 244

UC L=10.53

__ MR=3.22

LC L=0

Root Cause / Quick Win Root

Tools Used

cause: cause: cause:

Detailed process mapping Measurement Systems Analysis Value Stream Mapping Data Collection Planning Basic Statistics Process Capability Histograms

Time Series Plot Probability Plot Pareto Analysis Operational Definitions 5s Pull Control Charts

Quick Win #1

Root

Quick Win #2

Root

Quick Win #3

Bonacorsi Consulting

4

Graphical Analysis SummaryNormal Non-Normal Average Median or Q1 or Q3

Analyze

Data is Continuous: ?? Data Points Collected Between XX/XX/XX and XX/XX/XX Normality Central Tendency VariationStd. Dev (long term) Span (1/99) or Stability Factor (Q1/Q3)

Enter Key Slide Take Away (Key Point) HereBonacorsi Consulting5

TPC AnalysisTechnical-Political-Cultural (TPC) Analysis Sources Of Resistance Definition Causes Of Resistance Rating Examples

Analyze

Technical

Political

Cultural

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Sources of VariationDefect Overproduction Area1 Subarea1 Subarea1 Transportation Area1

Analyze

WastheActionworkout performed#1?

NVAArea1 Subarea1 Waiting

Area1 Subarea1 Processing Motion

Area1 Subarea1 Inventory

WastheActionworkout performed#2?

Enter Key Slide Take Away (Key Point) HereBonacorsi Consulting7

Cause and Effect MatrixImportance Rating Score Blank = no correlation 1 = remote correlation 3 = moderate correlation 9 = strong correlationItem Speed to Decision Cooperation, Seats Schedule Reach Meets Clarity, Feedback Available Options Decision Req Accuracy 10 7 6 7 10 9 Budget for Options 5 Funding Source Identified Process Outputs Correlation of Input to Output Process Step Process Inputs To what extent does variability in these process steps/inputs impact variability across these outputs ? Develop Options Customer schedule & including price & requirements schedule Approve options Proper decision makers Inventory Search of Database, tribal known Vacant Space knowledge Interview Customer to Form Understand Form Brief customer on options, costs, schedule options options, funding vehicle Supervisor Approval Peer Review Approval Fill out Forms Accept Form Create Spreadsheet Dept Approval Approval Mgr 1 Approval Mgr 2 Identify Need Assign to Team Mark Up Floor Plan Notification Forms to requestor and Log in issue Prepare brief 9 9 9 3 9 9 9 9 3 3 3 3 3 3 3 1 9 9 1 9 9 9 9 3 3 9 3 3 3 1 9 9 9 9 9 3 3 9 9 1 Importance 7 Rating Total Higher score = 3 9 459 392 261 217 216 118 111 108 51 30 30 30 30 21 21 0 0 0 0

Analyze

Wastheratingofimportance donebyKanoanalysis? Werealltheprocesssteps identified? Whatisthe%ofquickhits causingtheproblem?

16 18 15 14 17 8 7 11 12 3 4 5 6 1 13 2 9 19 19

3 3

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Run ChartsWhen the points are connected with a line, a run ends when the line crosses the median

Analyze

A run, in this case, is one or more consecutive points on the same side of the median

Median = 381 Run about the Median, can you count the other 8? There is a no nonrandom influence acting upon this process. There is no trend

Longest Run about the median

There is a nonrandom influence acting upon this process that is creating clustering

There is a no nonrandom influence acting upon this process. There is no Oscillation

Enter Key Slide Take Away (Key Point) HereBonacorsi Consulting9

Pareto Plot150

Analyze

Pareto Chart100 80

Count

100

60 40

50 20 0 0u So th r No th s Ea t he Ot rs

DefectCount Percent Cum %

100 58.5 58.5

50 29.2 87.7

15 8.8 96.5

6 3.5 100.0

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Percent

Scatter Plot

Analyze

Average Expenses decrease as Sales Increase

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Linear RegressionFitted Line PlotWait Time = 32.05 + 0.5825 D eliveries 55 S R-Sq R-Sq(adj)

Analyze

95% confident that 94.1% of the variation in Wait Time is from the Qty of Deliveries

1.11885 94.1% 93.9%

50 W i Tm at i e

45

40

35 10 15 20 25 Deliveries 30 35

Enter Key Slide Take Away (Key Point) HereBonacorsi Consulting12

One-Sample T-Test and Dot PlotDotplot of Improve Data(with Ho and 95% t-confidence interval for the mean)

Analyze

[

_ X Ho

]

This Dot Plot graphically displays 95% confidence intervals that the data will fall between 23.45 and 32.75 for response time (see the red brackets and red line). It also indicates that the Mean (Red X) is at 28.1. The blue Ho marks the Target Mean.Hypothesis Test: Is the Improve data set mean different from the Target Mean mean of 30 minutes?

We Are 95% Confident The Improve Mean Is Not Statistically Different0 10 20 30 40 50

Improve Data

The test statistic, T, for Ho: mean = 30 is calculated as 0.84. The P-Value of this test, or the probability of obtaining more extreme value of the test statistic by chance if the null hypothesis was true, is 0.410 (> 0.05). This is called the attained significant level, or P-Value. Therefore, Accept Ho, which means we conclude that the Improve data set mean (28.1) is NOT different than the Target mean (30).

One-Sample T: Improve Data Test of mu = 30 vs mu not = 30 Variable Improve Data Variable Improve Data N 30 Mean 28.10 StDev 12.45 T -0.84 P 0.410 SE Mean 2.27

95.0% CI (23.45, 32.75)

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Test for Equal Variance

Analyze

Test for Equal Variance Confirms Payroll Input Type Cycle Time is Significant

The spread of the data is statistical greater for completing the payroll form than the Antenna time tracking.

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One Way ANOVA

Boxplots of Net Hour by Part/No(means are indicated by solid circles)

Analyze

Net Hours Call Open

After further investigation, possible reasons proposed by the team are supplier backorders, lack of technician certifications and the distance from the supplier to the client site.

150

Boxplot: Part/ No Part Impact on Ticket Cycle Time

100

It is also caused by the need for technicians to make a second visit to the end user to 0 complete the part Part/No Part replacement. Next step will be for the of Variance for Net Hour Analysis team to confirm these suspected root causes. Source DF SS MS FPart/No Error Total Level No Part Part 1 69 70 N 27 44 7421 59194 66615 Mean 21.99 43.05 29.29 7421 858 StDev 19.95 33.70 8.65

50

No Part

P 0.004

Pooled StDev =

Individual 95% CI's For Mean --+---------+---------+---------+---(--------*---------) (------*------) --+---------+---------+---------+---12 24 36 48

Because the pvalue